In this News, we focus on the new features added in Stata 13.1 — a free update
to Stata 13. Power and sample-size analysis is now available for ANOVA, and
you can extend the power command with your own tests and statistics;
censored outcomes can now be modeled with endogenous covariates, sample
selection, random effects, and more; and several new features have been
added for analyzing univariate time series.
That is just what we added from Stata 13 to 13.1.
If you haven't already upgraded
to Stata 13, you are missing one of the most exciting releases of Stata ever.

Censored outcomes. Models with censored outcomes and interval-measured
outcomes can now include endogenous covariates, sample selection,
random effects and coefficients, treatment effects (ATEs), multivariate
models, unobserved components, and endogenous switching. All of these
features may be combined in a model.

Power and sample size. The power command that was introduced in Stata
13 has new methods for analyzing one-way, two-way, and repeated measures ANOVA
models. Like other power methods, you can compute (1) sample size, (2) power,
or (3) effect size. Compute any of the three given the other two.

Time series. IRFs (impulse-response functions) and parametric
autocorrelations can now be estimated after ARIMA and ARFIMA models.
Stability checks are also available for ARIMA models, and parametric spectral
densities are available for seasonal models.

If you don't already have Stata 13,
Stata 13 adds features and statistics for virtually every user in every field.
Here are the highlights.

Treatment effects. You can now estimate the effect of treatments such as
a new drug regimen, a surgical procedure, or a training program using
inverse-probability weights (IPW), propensity-score matching, doubly robust
methods, and more. Handle binary or multivalued treatments.

Multilevel models and panel data. Need to handle binary, ordered, count,
and categorical outcomes in panel or repeated-measures data? Now estimate
models with random effects or random coefficients, including mulitlevel and
crossed models.

Power and sample size. Get tables, graphs, or both at the click of a
button. Enter lists of known or possible values, and solve for power, sample
size, minimum detectable effect, or effect size. Do everything from
an integrated Control Panel.

Forecasting. Estimate any number of models and produce time-series
forecasts from all the models. Create dynamic or one-step-ahead forecasts.
Compare alterative scenarios, and more.

Project Manager. Keep your Stata projects organized. Filter on
filename, and click to open or run do-files, ado-files, datasets, raw files,
graphs, etc. And so much more.

And there are many more substantial additions, such as effect sizes, Poisson
regression with endogenous regressors, probit with sample selection, more
univariate time series, and import delimited with preview.

Stata 13.1, a free update to Stata 13, adds three new
methods for power and sample-size analysis of ANOVA
models—oneway, twoway, and repeated:

power oneway
performs analyses for one-way ANOVA

power twoway
performs analyses for two-way ANOVA

power repeated
performs analyses for repeated-measures ANOVA

These new facilities work just like the existing facilities
for comparisons of means, proportions, correlations, and
variances. You can specify single values or ranges of values
for power and effect size to compute required sample size.
You can specify sample size and effect size to compute
power. Or you can specify power and sample size to
compute effect size.

New with the Stata 13.1 update, you can now estimate models with censored or
interval-measured Gaussian outcomes that also include Heckman-style selection,
endogenous treatments to obtain average treatment effects (ATEs), covariate
measurement error, and unobserved components. You can include endogenous
regressors in any part of the models. You can also estimate these models in
a panel-data or multilevel-data context with random effects (intercepts) and
random coefficients in any part or all parts of the model. All of these
models can be estimated as parts of larger multivariate systems. Censored or
interval-measured outcomes can even participate in endogenous switching
models.

In some cases, you may want to compute sample size or power
yourself. For example, you may need to do this by simulation,
or you may want to use a method that is not available in any
software package. power makes it easy for you to add your
own method. All you need to do is to write a program that
computes sample size, power, or effect size, and the power
command will do the rest for you. It will deal with the support
of multiple values in options and with automatic generation of
graphs and tables of results.

July 31-August 1, 2014

Come join us in historic Boston, home to Fenway Park
and the Harvard Museum of Natural History, for two
days of networking and Stata exploration. Don't miss
this opportunity to connect with colleagues and fellow
researchers as well as Stata developers.

Timberlake (Portugal) and the Faculty of Economics at the University of
Porto are jointly organizing a set of applied econometrics courses using
Stata. The aim of these courses is to familiarize the participants with the
basic econometric tools commonly used in applied research. The courses
include a quick discussion of the relevant econometric theory as well as an
in-depth discussion of empirical applications using real data. The course
will take place at FEP, University of Porto, on January 21-24, 2014.

Discovering Structural Equation Modeling Using Stata, Revised Edition is an
excellent resource both for those who are new to SEM and for those who are
familiar with SEM but new to fitting these models in Stata. It is useful as
a text for courses covering SEM as well as for researchers performing SEM.

The Revised Edition includes output, syntax, and instructions for
fitting models with the SEM Builder that have been updated for Stata 13.

The third edition of Applied Logistic Regression, by David W. Hosmer, Jr.,
Stanley Lemeshow, and Rodney X. Sturdivant, is the definitive reference on
logistic regression models.

Most of the analyses in the book were performed using
Stata and can be replicated using Stata and the data from
the text. Also noteworthy is the book's use of multinomial
fractional polynomial models that can be fit using Stata's
mfp command.

Econometric Analysis of Panel Data, Fifth Edition, by Badi H. Baltagi, is a
standard reference for performing
estimation and inference on panel datasets from an
econometric standpoint. This book provides a rigorous
introduction to standard panel estimators as well as concise
explanations of many newer, more advanced techniques.

Because of its wide range of topics and detailed exposition, Econometric
Analysis of Panel Data, Fifth Edition, can serve as both a graduate-level
textbook and a handy desk reference for seasoned researchers.

Applied Longitudinal Data Analysis for Epidemiology: A Practical Guide,
Second Edition, by Jos W. R. Twisk, provides a practical introduction to
the estimation techniques used by epidemiologists for longitudinal data.

Be eco-friendly and go paperless.
Have the News delivered straight to your inbox.

Sign up to receive future issues of the Stata News in
electronic format before it arrives in the mail. We will
send you an email with the News content as soon as it is
available. The electronic format of the Stata News has all
the same information as the printed version. The only
difference is the convenience.